
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "120"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 91 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 87 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	1.08%	0.00%	1.592953	-0.616832	0.350652	-1.115793	0.662622	-0.743115	0.160492	-0.661601	0.7883	0.8021	0.2423	2.916095	3.348663
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	0.00%	1.665176	0.222727	1.170321	-0.835952	0.055045	-0.040235	1.746267	2.187467	0.7390	0.7374	0.2480	2.708560	2.539036
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.414508	-1.198555	0.923140	0.939623	1.990299	-1.102912	1.508966	-0.167827	0.0295	0.0293	0.0017	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.092747	8.524479	0.880687	0.576847	20.835681	24.683315	21.385468	24.471451	0.6628	0.6819	0.3422	12.385009	10.784076
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.527643	1.981789	1.747227	-0.730587	4.074347	2.872425	9.783396	11.774204	0.6734	0.6905	0.3507	8.409733	7.941862
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	1.614048	-0.587483	2.127784	-0.900376	0.188802	-0.726302	0.655423	-0.693582	0.7154	0.7168	0.2794	1.929685	1.840195
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	2.126262	-0.319978	2.582971	-0.480626	0.977709	0.686550	0.799212	-0.035369	0.7813	0.7748	0.2103	4.276951	3.136378
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	0.803359	0.821994	3.667219	-0.654083	-0.522816	-0.558005	7.004086	7.587625	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.601617	0.115399	0.506012	0.281555	-0.354567	0.983470	4.886984	6.465529	0.6554	0.6752	0.3532	5.966660	5.697579
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.997388	5.181185	0.644446	1.381859	2.183055	4.248960	2.697620	0.329755	0.0305	0.0298	0.0012	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.715768	20.799912	5.614067	3.402530	5.602344	12.075685	17.448489	17.363743	0.6572	0.6767	0.3508	0.000000	0.000000
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.714445	2.428203	2.233126	-0.602939	2.328170	3.218877	6.689438	7.207938	0.6642	0.6838	0.3675	0.000000	0.000000
2459582	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.437569	2.300257	1.371199	-0.495098	1.509943	1.905833	7.970020	9.424806	0.6687	0.6849	0.3666	8.269121	6.587399
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	19.46%	8.11%	0.001552	-0.151483	2.349966	-0.953076	-0.743871	-0.365081	1.259619	0.786720	0.6783	0.6959	0.3684	2.283085	2.012670
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.440202	10.935818	1.581640	-0.272916	5.617291	6.485869	24.121815	22.789622	0.6616	0.6793	0.3501	0.000000	0.000000
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.017022	0.736473	3.241550	-0.445334	0.314977	1.289114	4.124840	6.174964	0.6640	0.6846	0.3754	6.215328	5.682004
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.155878	-0.835095	-0.331739	1.026490	-0.162438	-0.451425	0.068889	-1.730482	0.0284	0.0278	0.0014	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.112290	-0.772153	3.210241	-0.779147	0.015211	-0.507445	1.152195	0.765957	0.6705	0.6920	0.3746	1.911428	1.658344
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	0.00%	0.307204	-0.676672	2.387557	-0.353986	0.298189	-0.491389	1.508989	0.443314	0.6949	0.7164	0.3762	1.618091	1.544040
2459575	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.078023	-0.759788	2.745755	-0.331112	1.195232	-0.492823	4.018576	-0.371826	0.7594	0.7662	0.3212	8.776613	7.713638
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	1.08%	0.981266	-0.458930	0.813574	-0.428665	-0.670046	-0.678936	-0.377205	-0.502539	0.6926	0.7100	0.3756	1.725321	1.569737
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.415012	5.710222	2.688806	-0.517365	3.583844	5.674153	23.589220	29.020414	0.6835	0.7022	0.3569	8.185549	6.717172
2459572	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.397057	5.204366	1.818301	-0.547981	2.123062	3.725723	33.002772	44.939748	0.6946	0.7125	0.3501	11.808555	9.606614
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.701351	-0.721077	-0.092080	0.971921	1.942769	-0.063931	0.101996	-1.798831	0.0374	0.0289	0.0032	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.54%	1.566951	-0.873444	1.015665	-0.492881	0.961674	-0.962013	0.732219	-0.696296	0.7708	0.7799	0.2265	2.598736	2.194010
2459566	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.478676	3.325034	4.961827	-0.589434	5.650990	3.311580	28.063416	17.737301	0.6718	0.6956	0.3760	9.969161	7.399823
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.684108	-0.538356	-0.558639	-0.096327	4.336892	0.204608	2.378490	2.406677	0.7026	0.7120	0.3822	3.606146	2.996344
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.156996	-0.699482	0.783846	0.077197	3.226977	-0.783707	2.429002	0.682011	0.6986	0.7109	0.3811	1.766578	1.640155
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.223235	-0.659092	1.665497	0.130469	0.153267	0.514834	1.526141	0.811987	0.6948	0.7145	0.3827	1.829084	1.568989
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.118855	-0.573116	1.304868	0.031292	-0.224718	0.337710	0.879749	0.382264	0.7019	0.7194	0.3758	1.813181	1.604244
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.147957	-0.791478	1.726387	-0.014232	0.044440	0.606353	0.883427	0.242815	0.6924	0.7118	0.3905	1.863196	1.577504
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.54%	-0.009595	-0.867371	1.568060	-0.115476	0.011343	0.466707	1.103062	0.309418	0.6929	0.7104	0.3755	1.822682	1.568020
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.612690	-0.674364	2.028320	-0.192907	-0.495748	-0.876294	2.315749	1.483072	0.6937	0.7084	0.3848	1.798462	1.596298
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0292	0.0297	0.0012	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.639616	-0.437831	1.267474	-0.214958	-0.508268	-0.661053	-0.080809	-0.454305	0.6932	0.7096	0.3792	1.733527	1.572517
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7003	0.7156	0.3870	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6965	0.7118	0.3852	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.131133	-0.662901	1.513262	-0.987216	2.580886	-0.822608	1.148138	1.926180	0.7078	0.7174	0.3757	1.058120	1.171035
2459551	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.864246	0.071714	0.676847	-0.280672	0.548104	0.807439	9.487466	7.625385	0.6973	0.7127	0.3653	4.145033	3.312866
2459550	digital_ok	-	96.71%	96.71%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0458	0.0448	0.0012	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.085190	2.323210	0.751215	-0.694042	3.341965	2.929488	24.378415	19.305792	0.6897	0.7083	0.3783	5.230066	4.297556
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	0.201059	-1.066291	0.404818	-0.449416	0.336698	-0.526418	2.114099	1.571380	0.7485	0.7604	0.3436	2.043701	1.640438
2459541	digital_ok	0.00%	1.79%	1.79%	0.00%	21.88%	0.00%	0.003132	-1.202223	0.993959	-0.986671	2.704141	-1.121665	1.003939	-0.366413	0.7343	0.7463	0.3169	1.521191	1.459213
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	16.96%	0.958031	-0.647330	0.580621	0.007721	1.343924	0.266507	2.977868	1.672036	0.7486	0.7585	0.3263	2.000931	1.619517
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.964067	1.025482	2.728951	-0.857469	1.994023	1.134151	18.102964	14.140336	0.7222	0.7327	0.4019	6.444913	5.452177
2459535	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.719424	5.353093	0.458726	-0.298156	3.524941	2.463606	22.443135	23.629028	0.7770	0.7787	0.3242	3.853226	3.390689
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.45%	0.261961	-0.788822	0.720912	-0.008091	-0.083224	-0.481434	1.969602	1.687828	0.7750	0.7727	0.3347	1.883482	1.684688
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	0.566986	-0.810538	1.057674	-0.686922	-0.789565	-0.557779	1.921205	1.628750	0.7157	0.7351	0.3873	1.701780	1.530448
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.588261	6.998002	1.492549	-0.411733	7.738428	9.437487	30.052141	32.606084	0.7183	0.7341	0.3608	5.377647	4.828021
2459530	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.947868	1.184175	0.683390	-0.207873	2.464853	2.846959	14.381079	16.500778	0.7230	0.7384	0.3784	4.554257	4.284642
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.950814	-0.956569	2.052541	-0.418787	0.897980	-1.234191	1.402336	0.419081	0.9131	0.9431	0.2770	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	12.24%	2.04%	-0.237766	-1.495141	2.335533	-0.692122	-1.045224	-1.176728	0.545921	-0.223717	nan	nan	nan	2.180883	1.937978
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.252331	-1.221640	1.819205	-0.498971	-0.860027	-0.487276	3.289465	0.869761	0.6708	0.7287	0.3390	1.683452	2.198068
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.657862	-1.002658	-0.229503	0.315603	0.480956	-1.230552	-0.068046	-1.355633	0.0523	0.0333	0.0035	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.712440	-0.629096	0.665114	-0.000579	-0.327313	-0.721165	0.873868	-0.126805	0.9245	0.9559	0.1574	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.406044	-0.528110	1.101133	0.120848	-0.062480	-0.787713	0.221238	-0.669626	0.8885	0.8690	0.1934	6.635802	5.300713
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	0.805321	-0.610114	1.857424	-0.161267	2.988585	-0.682121	1.133647	0.153560	0.8885	0.8806	0.4053	0.000000	0.000000
2459501	digital_ok	0.00%	0.00%	0.00%	0.00%	76.74%	0.00%	0.166162	-0.732134	0.312394	-0.464994	0.037870	-0.811235	0.584960	0.006481	0.8793	0.9066	0.3613	0.000000	0.000000
2459500	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	5.26%	-0.104222	-1.429958	-0.062481	-0.504460	1.164465	-0.302226	0.865215	0.475243	0.8864	0.9011	0.3540	0.000000	0.000000
2459499	digital_ok	0.00%	0.00%	0.00%	0.00%	97.37%	0.00%	-0.084905	-1.233845	0.824474	0.017188	1.236486	-0.057114	3.004441	1.406828	0.8946	0.9441	0.1597	0.000000	0.000000
2459498	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	-0.033208	-1.281042	0.539351	-0.218285	1.113193	-0.183430	3.296582	0.920076	0.8988	0.9033	0.3777	0.000000	0.000000
2459497	digital_ok	0.00%	0.00%	0.00%	0.00%	97.37%	2.63%	-0.138593	-1.244970	0.484228	-0.444110	1.407995	-0.796496	2.044837	-0.109792	0.9205	0.9383	0.2946	0.000000	0.000000
2459496	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	-0.473198	-1.376770	0.972913	-0.095443	2.056765	-0.014199	2.024808	0.770757	0.8811	0.8936	0.3914	0.000000	0.000000
2459495	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	5.26%	-0.327349	-1.385310	0.793301	-0.154068	1.965221	-0.217762	1.449142	0.633247	0.8754	0.8889	0.4266	0.000000	0.000000
2459494	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	5.26%	-0.231125	-1.322024	0.628486	-0.037075	1.646651	-0.016893	0.405125	-0.352421	0.8712	0.8841	0.4398	0.000000	0.000000
2459492	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.017839	-1.039787	0.183558	-1.634379	-0.997389	-0.548026	0.128268	-1.164243	0.7381	0.7991	0.3406	0.000000	0.000000
2459491	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.398863	-0.934355	0.371735	-1.496061	-0.919397	-1.137930	0.122373	-0.650536	0.7774	0.8127	0.2763	0.000000	0.000000
2459490	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.464525	-0.709159	-0.050306	-1.942194	-0.701870	-1.069930	0.991081	-0.382864	0.7483	0.8050	0.3598	0.000000	0.000000
2459489	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.235023	-1.250528	0.173025	-1.545229	-0.847559	-0.318601	3.441980	0.749200	0.7658	0.8105	0.3070	0.000000	0.000000
2459488	digital_ok	0.00%	0.00%	0.00%	0.00%	85.71%	14.29%	-0.320908	-1.339299	0.494575	-1.115916	-0.903789	-0.707099	1.019970	-0.445433	0.7649	0.8136	0.3427	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 120 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 120, Node 8:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 120: 2459615

Antenna 120: 2459614

Antenna 120: 2459613

Antenna 120: 2459612

Antenna 120: 2459611

Antenna 120: 2459610

Antenna 120: 2459609

Antenna 120: 2459608

Antenna 120: 2459607

Antenna 120: 2459605

Antenna 120: 2459604

Antenna 120: 2459603

Antenna 120: 2459602

Antenna 120: 2459598

Antenna 120: 2459597

Antenna 120: 2459596

Antenna 120: 2459595

Antenna 120: 2459594

Antenna 120: 2459593

Antenna 120: 2459592

Antenna 120: 2459591

Antenna 120: 2459590

Antenna 120: 2459589

Antenna 120: 2459588

Antenna 120: 2459587

Antenna 120: 2459586

Antenna 120: 2459585

Antenna 120: 2459584

Antenna 120: 2459583

Antenna 120: 2459582

Antenna 120: 2459581

Antenna 120: 2459580

Antenna 120: 2459579

Antenna 120: 2459578

Antenna 120: 2459577

Antenna 120: 2459576

Antenna 120: 2459575

Antenna 120: 2459574

Antenna 120: 2459573

Antenna 120: 2459572

Antenna 120: 2459571

Antenna 120: 2459570

Antenna 120: 2459569

Antenna 120: 2459566

Antenna 120: 2459565

Antenna 120: 2459564

Antenna 120: 2459563

Antenna 120: 2459562

Antenna 120: 2459561

Antenna 120: 2459560

Antenna 120: 2459559

Antenna 120: 2459558

Antenna 120: 2459556

Antenna 120: 2459552

Antenna 120: 2459551

Antenna 120: 2459549

Antenna 120: 2459542

Antenna 120: 2459541

Antenna 120: 2459540

Antenna 120: 2459536

Antenna 120: 2459535

Antenna 120: 2459534

Antenna 120: 2459533

Antenna 120: 2459532

Antenna 120: 2459530

Antenna 120: 2459527

Antenna 120: 2459524

Antenna 120: 2459523

Antenna 120: 2459522

Antenna 120: 2459513

Antenna 120: 2459508

Antenna 120: 2459506

Antenna 120: 2459505

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶